The converted measurement Kalman filter (CMKF) converts the raw measurement into Cartesian coordinates prior to tracking. Consequently, the converted measurement is a linear function of the state, allowing for the use of a standard (linear) Kalman filter. The CMKF with a bias correction term has previously been shown to have superior performance over the converted prediction approach [1]}. [2]} performs elliptic-to-Cartesian coordinate conversion of the measurements and evaluates the noise affecting Cartesian coordinate by a non-diagonal matrix. [3]} approximates the non-Gaussian Cartesian error distribution by a single Gaussian distribution with first two moments matched using first and second order Taylor expansion of the conversion, and fits a Gaussian mixture distribution model to the converted measurement. Converted measurement sigma point Kalman filter for bistatic sonar and radar tracking [4]} uses a sigma point transform to estimate the conversion bias and the converted measurement error covariance, which do not need to be explicitly derived. Analogously, various approaches for bistatic to Cartesian conversion using cubature points were proposed, and were compared in [5]}. The only known works that directly address the bias issue in bistatic coordinate conversion are [6]}, which gives the debiased conversion, and [7]}, which uses the second-order unscented transform. The challenges of CMKF are debiasing the converted measurement and approximating the converted measurement error covariance. Compared with the monostatic radar [8]}, [9]}, [10]}, [11]}, these two problems in the bistatic case have not been addressed well.
